from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2021-01-02 14:09:13.920857
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64(TODAY),
'orange', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Sat, 02, Jan, 2021
Time: 14:09:18
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -44.4665
Nobs: 159.000 HQIC: -45.4981
Log likelihood: 1732.68 FPE: 8.60390e-21
AIC: -46.2036 Det(Omega_mle): 4.96923e-21
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.459326 0.157910 2.909 0.004
L1.Burgenland 0.137678 0.080371 1.713 0.087
L1.Kärnten -0.236107 0.064670 -3.651 0.000
L1.Niederösterreich 0.110016 0.187549 0.587 0.557
L1.Oberösterreich 0.253715 0.160268 1.583 0.113
L1.Salzburg 0.174458 0.082798 2.107 0.035
L1.Steiermark 0.081583 0.115206 0.708 0.479
L1.Tirol 0.149160 0.077020 1.937 0.053
L1.Vorarlberg 0.006832 0.073569 0.093 0.926
L1.Wien -0.119832 0.154962 -0.773 0.439
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.513607 0.204264 2.514 0.012
L1.Burgenland 0.011262 0.103963 0.108 0.914
L1.Kärnten 0.367217 0.083653 4.390 0.000
L1.Niederösterreich 0.132409 0.242603 0.546 0.585
L1.Oberösterreich -0.186606 0.207314 -0.900 0.368
L1.Salzburg 0.187465 0.107103 1.750 0.080
L1.Steiermark 0.251966 0.149024 1.691 0.091
L1.Tirol 0.142767 0.099628 1.433 0.152
L1.Vorarlberg 0.177548 0.095164 1.866 0.062
L1.Wien -0.582022 0.200450 -2.904 0.004
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.294485 0.068783 4.281 0.000
L1.Burgenland 0.106267 0.035008 3.036 0.002
L1.Kärnten -0.026288 0.028169 -0.933 0.351
L1.Niederösterreich 0.070970 0.081693 0.869 0.385
L1.Oberösterreich 0.288928 0.069810 4.139 0.000
L1.Salzburg -0.002149 0.036065 -0.060 0.952
L1.Steiermark -0.020904 0.050182 -0.417 0.677
L1.Tirol 0.088810 0.033548 2.647 0.008
L1.Vorarlberg 0.126328 0.032045 3.942 0.000
L1.Wien 0.080128 0.067498 1.187 0.235
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.203849 0.079682 2.558 0.011
L1.Burgenland -0.012293 0.040555 -0.303 0.762
L1.Kärnten 0.022549 0.032632 0.691 0.490
L1.Niederösterreich 0.025911 0.094638 0.274 0.784
L1.Oberösterreich 0.412955 0.080872 5.106 0.000
L1.Salzburg 0.096416 0.041780 2.308 0.021
L1.Steiermark 0.182564 0.058133 3.140 0.002
L1.Tirol 0.033009 0.038864 0.849 0.396
L1.Vorarlberg 0.095949 0.037123 2.585 0.010
L1.Wien -0.062363 0.078194 -0.798 0.425
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.594059 0.166131 3.576 0.000
L1.Burgenland 0.073141 0.084555 0.865 0.387
L1.Kärnten 0.002958 0.068036 0.043 0.965
L1.Niederösterreich -0.043539 0.197312 -0.221 0.825
L1.Oberösterreich 0.156869 0.168611 0.930 0.352
L1.Salzburg 0.053572 0.087108 0.615 0.539
L1.Steiermark 0.113775 0.121204 0.939 0.348
L1.Tirol 0.210388 0.081029 2.596 0.009
L1.Vorarlberg 0.004399 0.077398 0.057 0.955
L1.Wien -0.147800 0.163029 -0.907 0.365
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.158813 0.115641 1.373 0.170
L1.Burgenland -0.025677 0.058857 -0.436 0.663
L1.Kärnten -0.013264 0.047359 -0.280 0.779
L1.Niederösterreich 0.174522 0.137346 1.271 0.204
L1.Oberösterreich 0.395566 0.117368 3.370 0.001
L1.Salzburg -0.028355 0.060635 -0.468 0.640
L1.Steiermark -0.045897 0.084368 -0.544 0.586
L1.Tirol 0.188862 0.056403 3.348 0.001
L1.Vorarlberg 0.039816 0.053876 0.739 0.460
L1.Wien 0.163768 0.113482 1.443 0.149
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.238442 0.145120 1.643 0.100
L1.Burgenland 0.066902 0.073861 0.906 0.365
L1.Kärnten -0.044198 0.059431 -0.744 0.457
L1.Niederösterreich -0.026486 0.172357 -0.154 0.878
L1.Oberösterreich -0.100389 0.147286 -0.682 0.495
L1.Salzburg 0.003807 0.076091 0.050 0.960
L1.Steiermark 0.382429 0.105875 3.612 0.000
L1.Tirol 0.518126 0.070781 7.320 0.000
L1.Vorarlberg 0.194692 0.067610 2.880 0.004
L1.Wien -0.230287 0.142410 -1.617 0.106
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.121892 0.170066 0.717 0.474
L1.Burgenland 0.015335 0.086558 0.177 0.859
L1.Kärnten -0.113839 0.069648 -1.634 0.102
L1.Niederösterreich 0.220130 0.201986 1.090 0.276
L1.Oberösterreich 0.010301 0.172605 0.060 0.952
L1.Salzburg 0.220586 0.089171 2.474 0.013
L1.Steiermark 0.143282 0.124074 1.155 0.248
L1.Tirol 0.093366 0.082948 1.126 0.260
L1.Vorarlberg 0.014389 0.079232 0.182 0.856
L1.Wien 0.285660 0.166890 1.712 0.087
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.587491 0.093514 6.282 0.000
L1.Burgenland -0.019998 0.047595 -0.420 0.674
L1.Kärnten 0.000896 0.038297 0.023 0.981
L1.Niederösterreich -0.007605 0.111065 -0.068 0.945
L1.Oberösterreich 0.279604 0.094910 2.946 0.003
L1.Salzburg 0.010472 0.049032 0.214 0.831
L1.Steiermark 0.000802 0.068224 0.012 0.991
L1.Tirol 0.077453 0.045611 1.698 0.089
L1.Vorarlberg 0.169014 0.043567 3.879 0.000
L1.Wien -0.093229 0.091767 -1.016 0.310
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.139639 -0.003220 0.205535 0.243955 0.058665 0.097291 -0.082749 0.161787
Kärnten 0.139639 1.000000 -0.005847 0.186173 0.130070 -0.145582 0.172790 0.031077 0.297635
Niederösterreich -0.003220 -0.005847 1.000000 0.261800 0.079934 0.201417 0.099011 0.041933 0.353158
Oberösterreich 0.205535 0.186173 0.261800 1.000000 0.276194 0.291346 0.109749 0.075894 0.108655
Salzburg 0.243955 0.130070 0.079934 0.276194 1.000000 0.147107 0.073892 0.081398 -0.021849
Steiermark 0.058665 -0.145582 0.201417 0.291346 0.147107 1.000000 0.103920 0.086705 -0.129443
Tirol 0.097291 0.172790 0.099011 0.109749 0.073892 0.103920 1.000000 0.150253 0.138644
Vorarlberg -0.082749 0.031077 0.041933 0.075894 0.081398 0.086705 0.150253 1.000000 0.102446
Wien 0.161787 0.297635 0.353158 0.108655 -0.021849 -0.129443 0.138644 0.102446 1.000000